The word“geda”,as a typical example of partitive in Shaanxi local dialect,appears 40 times in Jia Pingwa’s novel Daideng,but with varied translation equivalents in its English version The Lantern Bearer,where this ...The word“geda”,as a typical example of partitive in Shaanxi local dialect,appears 40 times in Jia Pingwa’s novel Daideng,but with varied translation equivalents in its English version The Lantern Bearer,where this article raised its doubts about how a proper translation of partitive can transmit culture in a more authentic and loyal way.From the perspective of cultural schema theory,this article takes The Lantern Bearer1 as the basis of text analysis to explore the translation strategy of the partitive“geda”in the context of cultural correspondence before relevant translation models are proposed with the purpose to shed light on Shaanxi local literature translation into English.展开更多
针对传统高斯分布估计算法(Gaussian estimation of distribution algorithms,GEDAs)中变量方差减小速度快、概率密度椭球体(Probability density ellipsoid,PDE)的长轴与目标函数的改进方向相垂直,从而导致算法搜索效率低、容易早熟收...针对传统高斯分布估计算法(Gaussian estimation of distribution algorithms,GEDAs)中变量方差减小速度快、概率密度椭球体(Probability density ellipsoid,PDE)的长轴与目标函数的改进方向相垂直,从而导致算法搜索效率低、容易早熟收敛这一问题,提出一种基于一般二阶混合矩的高斯分布估计算法.该算法利用加权的优秀样本预估高斯均值,并根据沿目标函数的改进方向偏移后的均值来估计协方差矩阵.理论和数值分析表明,这一简单操作可以在不增大算法计算量的前提下自适应地调整概率密度椭球体的位置、大小和长轴方向,提高算法的搜索效率.在14个标准函数上对所提算法进行了测试,由统计出的Cohen's d效应量指标可知该算法的全局寻优能力强于传统高斯分布估计算法;与当前先进的粒子群算法、差分进化算法相比,所提算法可以在相同的函数评价次数内获得9个函数的显著优解.展开更多
基金supported by the Scientific Research Funds of the Educational Department of Shaanxi Provincial Government(Grant No.16JK1731)Shaanxi International Chinese Education Research Association(Grant No.2022HZ1478).
文摘The word“geda”,as a typical example of partitive in Shaanxi local dialect,appears 40 times in Jia Pingwa’s novel Daideng,but with varied translation equivalents in its English version The Lantern Bearer,where this article raised its doubts about how a proper translation of partitive can transmit culture in a more authentic and loyal way.From the perspective of cultural schema theory,this article takes The Lantern Bearer1 as the basis of text analysis to explore the translation strategy of the partitive“geda”in the context of cultural correspondence before relevant translation models are proposed with the purpose to shed light on Shaanxi local literature translation into English.
文摘针对传统高斯分布估计算法(Gaussian estimation of distribution algorithms,GEDAs)中变量方差减小速度快、概率密度椭球体(Probability density ellipsoid,PDE)的长轴与目标函数的改进方向相垂直,从而导致算法搜索效率低、容易早熟收敛这一问题,提出一种基于一般二阶混合矩的高斯分布估计算法.该算法利用加权的优秀样本预估高斯均值,并根据沿目标函数的改进方向偏移后的均值来估计协方差矩阵.理论和数值分析表明,这一简单操作可以在不增大算法计算量的前提下自适应地调整概率密度椭球体的位置、大小和长轴方向,提高算法的搜索效率.在14个标准函数上对所提算法进行了测试,由统计出的Cohen's d效应量指标可知该算法的全局寻优能力强于传统高斯分布估计算法;与当前先进的粒子群算法、差分进化算法相比,所提算法可以在相同的函数评价次数内获得9个函数的显著优解.
